{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff1b5047",
   "metadata": {},
   "source": [
    "模块加入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cdd5a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv('data/RFM.csv')\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cd4f34",
   "metadata": {},
   "source": [
    "数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e9d4b59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a4708976",
   "metadata": {},
   "source": [
    "时间计算"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ac2ea7",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e11f06cc",
   "metadata": {},
   "source": [
    "三合一合并数据表，拼接"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea39edb",
   "metadata": {},
   "source": [
    "三列改名"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e0ca87",
   "metadata": {},
   "source": [
    "打分，再按照总分聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "898f8165",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "22e009fd",
   "metadata": {},
   "source": [
    "可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac94c02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6c14b449",
   "metadata": {},
   "source": [
    "不同图表输出"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
